arXiv subjects

Avimita Chatterjee

Publications and source records attributed to Avimita Chatterjee.

At least 19 recordsLinked to original sources

arXiv2026

No Tile Left Behind: Multiprogramming for Surface-Code Architectures

Fault-tolerant quantum computing (FTQC) is emerging as the architectural regime in which practical large-scale quantum workloads will execute. In this setting, however, multiprogramming is no longer a matter of partitioning a flat pool of qubits. Quantum error correction exposes a structured floorplan of data tiles, ancilla tiles, and magic-state service resources, so concurrent execution must account for compact placement, connectivity, routing headroom, and shared support infrastructure. This makes FTQC multiprogramming fundamentally harder than its NISQ counterpart: admission decisions can fragment the remaining floorplan, conservative reservations can waste ancilla, and dynamic contention across data, ancilla, and magic-state resources can degrade both throughput and quality of service. In this work, we develop a formal framework for FTQC multiprogramming that captures these structural constraints and their runtime implications. We formulate the baseline static allocation problem, extend it to limited-resource and online settings through hierarchy-aware scheduling policies, and further generalize it to cultivation-enabled architectures with dynamic magic-state generation. Through simulation on synthetic Clifford+T workloads, the proposed scheduler achieves a normalized system speedup of 3.1x, improving over prior FTQC multiprogramming baselines by ~29% while maintaining low mean slowdown.

quant-ph↗

arXiv2026

Toward designing workload-aware Surface Code Architectures

Practical quantum advantage is expected to depend on fault-tolerant quantum computing, although the architectural overhead needed to support fault tolerance is still extremely high. Prior FTQC designs generally emphasize either fast logical-qubit accessibility at the cost of significant qubit overhead, or high logical-qubit density at the cost of added workload latency. We propose an architecture that balances these competing objectives by placing surface-code patches around an ancilla-centric region, which yields nearly uniform ancilla access for all data qubits. Building on this design, we introduce a new workload-driven placement method that uses the $T$-gate profile of an application to determine an effective floorplan. We further provide a reconfigurable optimization for reducing the latency of $Y$-gate measurements on a per-workload basis. To improve flexibility, we also study concurrent execution of multiple programs on the same architecture. Numerical evaluation indicates that our approach keeps cycles per instruction near the optimal regime while reducing the number of required data tiles by up to $\sim21\%$, and achieves up to $\sim90\%$ efficiency when running 10 programs concurrently.

quant-ph↗

arXiv2025

Platform-Agnostic Modular Architecture for Quantum Benchmarking

We present a platform-agnostic modular architecture that addresses the increasingly fragmented landscape of quantum computing benchmarking by decoupling problem generation, circuit execution, and results analysis into independent, interoperable components. Supporting over 20 benchmark variants ranging from simple algorithmic tests like Bernstein-Vazirani to complex Hamiltonian simulation with observable calculations, the system integrates with multiple circuit generation APIs (Qiskit, CUDA-Q, Cirq) and enables diverse workflows. We validate the architecture through successful integration with Sandia's $\textit{pyGSTi}$ for advanced circuit analysis and CUDA-Q for multi-GPU HPC simulations. Extensibility of the system is demonstrated by implementing dynamic circuit variants of existing benchmarks and a new quantum reinforcement learning benchmark, which become readily available across multiple execution and analysis modes. Our primary contribution is identifying and formalizing modular interfaces that enable interoperability between incompatible benchmarking frameworks, demonstrating that standardized interfaces reduce ecosystem fragmentation while preserving optimization flexibility. This architecture has been developed as a key enhancement to the continually evolving QED-C Application-Oriented Performance Benchmarks for Quantum Computing suite.

quant-ph↗

arXiv2025

Design Automation in Quantum Error Correction

Quantum error correction (QEC) underpins practical fault-tolerant quantum computing (FTQC) by addressing the fragility of quantum states and mitigating decoherence-induced errors. As quantum devices scale, integrating robust QEC protocols is imperative to suppress logical error rates below threshold and ensure reliable operation, though current frameworks suffer from substantial qubit overheads and hardware inefficiencies. Design automation in the QEC flow is thus critical, enabling automated synthesis, transpilation, layout, and verification of error-corrected circuits to reduce qubit footprints and push fault-tolerance margins. This chapter presents a comprehensive treatment of design automation in QEC, structured into four main sections. The first section delves into the theoretical aspects of QEC, covering logical versus physical qubit representations, stabilizer code construction, and error syndrome extraction mechanisms. In the second section, we outline the QEC design flow, detailing the areas highlighting the need for design automation. The third section surveys recent advancements in design automation techniques, including algorithmic $T$-gate optimization, modified surface code architecture to incorporate lesser qubit overhead, and machine-learning-based decoder automation. The final section examines near-term FTQC architectures, integrating automated QEC pipelines into scalable hardware platforms and discussing end-to-end verification methodologies. Each section is complemented by case studies of recent research works, illustrating practical implementations and performance trade-offs. Collectively, this chapter aims to equip readers with a holistic understanding of design automation in QEC system design in the fault-tolerant landscape of quantum computing.

quant-ph↗

arXiv2025

Survival of the Optimized: An Evolutionary Approach to T-depth Reduction

Quantum Error Correction (QEC) is the cornerstone of practical Fault-Tolerant Quantum Computing (FTQC), but incurs enormous resource overheads. Circuits must decompose into Clifford+T gates, and the non-transversal T gates demand costly magic-state distillation. As circuit complexity grows, sequential T-gate layers ("T-depth") increase, amplifying the spatiotemporal overhead of QEC. Optimizing T-depth is NP-hard, and existing greedy or brute-force strategies are either inefficient or computationally prohibitive. We frame T-depth reduction as a search optimization problem and present a Genetic Algorithm (GA) framework that approximates optimal layer-merge patterns across the non-convex search space. We introduce a mathematical formulation of the circuit expansion for systematic layer reordering and a greedy initial merge-pair selection, accelerating the convergence and enhancing the solution quality. In our benchmark with ~90-100 qubits, our method reduces T-depth by 79.23% and overall T-count by 41.86%. Compared to the reversible circuit benchmarks, we achieve a 2.58x improvement in T-depth over the state-of-the-art methods, demonstrating its viability for near-term FTQC.

quant-ph↗

arXiv2025

Capturing Quantum Snapshots from a Single Copy via Mid-Circuit Measurement and Dynamic Circuit

We propose Quantum Snapshot with Dynamic Circuit (QSDC), a hardware-agnostic, learning-driven framework for capturing quantum snapshots: non-destructive estimates of quantum states at arbitrary points within a quantum circuit, which can then be classically stored and later reconstructed. This functionality is vital for introspection, debugging, and memory in quantum systems, yet remains fundamentally constrained by the no-cloning theorem and the destructive nature of measurement. QSDC introduces a guess-and-check methodology in which a classical model, powered by either gradient-based neural networks or gradient-free evolutionary strategie, is trained to reconstruct an unknown quantum state using fidelity from the SWAP test as the sole feedback signal. Our approach supports single-copy, mid-circuit state reconstruction, assuming hardware with dynamic circuit support and sufficient coherence time. We validate core components of QSDC both in simulation and on IBM quantum hardware. In noiseless settings, our models achieve average fidelity up to 0.999 across 100 random quantum states; on real devices, we accurately reconstruct known single-qubit states (e.g., Hadamard) within three optimization steps.

quant-ph↗

arXiv2025

Guess, SWAP, Repeat : Capturing Quantum Snapshots in Classical Memory

We introduce a novel technique that enables observation of quantum states without direct measurement, preserving them for reuse. Our method allows multiple quantum states to be observed at different points within a single circuit, one at a time, and saved into classical memory without destruction. These saved states can be accessed on demand by downstream applications, introducing a dynamic and programmable notion of quantum memory that supports modular, non-destructive quantum workflows. We propose a hardware-agnostic, machine learning-driven framework to capture non-destructive estimates, or "snapshots," of quantum states at arbitrary points within a circuit, enabling classical storage and later reconstruction, similar to memory operations in classical computing. This capability is essential for debugging, introspection, and persistent memory in quantum systems, yet remains difficult due to the no-cloning theorem and destructive measurements. Our guess-and-check approach uses fidelity estimation via the SWAP test to guide state reconstruction. We explore both gradient-based deep neural networks and gradient-free evolutionary strategies to estimate quantum states using only fidelity as the learning signal. We demonstrate a key component of our framework on IBM quantum hardware, achieving high-fidelity (approximately 1.0) reconstructions for Hadamard and other known states. In simulation, our models achieve an average fidelity of 0.999 across 100 random quantum states. This provides a pathway toward non-volatile quantum memory, enabling long-term storage and reuse of quantum information, and laying groundwork for future quantum memory architectures.

quant-ph↗

arXiv2025

A Practical Framework for Assessing the Performance of Observable Estimation in Quantum Simulation

Simulating dynamics of physical systems is a key application of quantum computing, with potential impact in fields such as condensed matter physics and quantum chemistry. However, current quantum algorithms for Hamiltonian simulation yield results that are inadequate for real use cases and suffer from lengthy execution times when implemented on near-term quantum hardware. In this work, we introduce a framework for evaluating the performance of quantum simulation algorithms, focusing on the computation of observables, such as energy expectation values. Our framework provides end-to-end demonstrations of algorithmic optimizations that utilize Pauli term groups based on k-commutativity, generate customized Clifford measurement circuits, and implement weighted shot distribution strategies across these groups. These demonstrations span multiple quantum execution environments, allowing us to identify critical factors influencing runtime and solution accuracy. We integrate enhancements into the QED-C Application-Oriented Benchmark suite, utilizing problem instances from the open-source HamLib collection. Our results demonstrate a 27.1% error reduction through Pauli grouping methods, with an additional 37.6% improvement from the optimized shot distribution strategy. Our framework provides an essential tool for advancing quantum simulation performance using algorithmic optimization techniques, enabling systematic evaluation of improvements that could maximize near-term quantum computers' capabilities and advance practical quantum utility as hardware evolves.

quant-ph↗

arXiv2025

The Q-Spellbook: Crafting Surface Code Layouts and Magic State Protocols for Large-Scale Quantum Computing

Quantum error correction is a cornerstone of reliable quantum computing, with surface codes emerging as a prominent method for protecting quantum information. Surface codes are efficient for Clifford gates but require magic state distillation protocols to process non-Clifford gates, such as T gates, essential for universal quantum computation. In large-scale quantum architectures capable of correcting arbitrary circuits, specialized surface codes for data qubits and distinct codes for magic state distillation are needed. These architectures can be organized into data blocks and distillation blocks. The system works by having distillation blocks produce magic states and data blocks consume them, causing stalls due to either a shortage or excess of magic states. This bottleneck presents an opportunity to optimize quantum space by balancing data and distillation blocks. While prior research offers insights into selecting distillation protocols and estimating qubit requirements, it lacks a tailored optimization approach. We present a framework for optimizing large-scale quantum architectures, focusing on data block layouts and magic state distillation protocols. We evaluate three data block layouts and four distillation protocols under three optimization strategies: minimizing tiles, minimizing steps, and achieving a balanced trade-off. Through a comparative analysis of brute force, dynamic programming, greedy, and random algorithms, we find that brute force delivers optimal results, while greedy deviates by 7% for minimizing steps and dynamic programming matches brute force in tile minimization. We observe that total steps increase with columns, while total tiles scale with qubits. Finally, we propose a heuristic to help users select algorithms suited to their objectives, enabling scalable and efficient quantum architectures.

quant-ph↗

arXiv2025

The Art of Optimizing T-Depth for Quantum Error Correction in Large-Scale Quantum Computing

Quantum Error Correction (QEC), combined with magic state distillation, ensures fault tolerance in large-scale quantum computation. To apply QEC, a circuit must first be transformed into a non-Clifford (or T) gate set. T-depth, the number of sequential T-gate layers, determines the magic state cost, impacting both spatial and temporal overhead. Minimizing T-depth is crucial for optimizing resource efficiency in fault-tolerant quantum computing. While QEC scalability has been widely studied, T-depth reduction remains an overlooked challenge. We establish that T-depth reduction is an NP-hard problem and systematically evaluate multiple approximation techniques: greedy, divide-and-conquer, Lookahead-based brute force, and graph-based. The Lookahead-based brute-force algorithm (partition size 4) performs best, optimizing 90\% of reducible cases (i.e., circuits where at least one algorithm achieved optimization) with an average T-depth reduction of around 51\%. Additionally, we introduce an expansion factor-based identity gate insertion strategy, leveraging controlled redundancy to achieve deeper reductions in circuits initially classified as non-reducible. With this approach, we successfully convert up to 25\% of non-reducible circuits into reducible ones, while achieving an additional average reduction of up to 11.8\%. Furthermore, we analyze the impact of different expansion factor values and explore how varying the partition size in the Lookahead-based brute-force algorithm influences the quality of T-depth reduction.

quant-ph↗

arXiv2024

Quantum Prometheus: Defying Overhead with Recycled Ancillas in Quantum Error Correction

Quantum error correction (QEC) is crucial for ensuring the reliability of quantum computers. However, implementing QEC often requires a significant number of qubits, leading to substantial overhead. One of the major challenges in quantum computing is reducing this overhead, especially since QEC codes depend heavily on ancilla qubits for stabilizer measurements. In this work, we propose reducing the number of ancilla qubits by reusing the same ancilla qubits for both X- and Z-type stabilizers. This is achieved by alternating between X and Z stabilizer measurements during each half-round, cutting the number of required ancilla qubits in half. This technique can be applied broadly across various QEC codes, we focus on rotated surface codes only and achieve nearly $25\%$ reduction in total qubit overhead. We also present a few use cases where the proposed idea enables the usage of higher-distance surface codes at a relatively lesser qubit count. Our analysis shows that the modified approach enables users to achieve similar or better error correction with fewer qubits, especially for higher distances ($d \geq 13$). Additionally, we identify conditions where the modified code allows for extended distances ($d + k$) while using the same or fewer resources as the original, offering a scalable and practical solution for quantum error correction. These findings emphasize the modified surface code's potential to optimize qubit usage in resource-constrained quantum systems.

quant-ph↗

arXiv2024

Magic Mirror on the Wall, How to Benchmark Quantum Error Correction Codes, Overall ?

Quantum Error Correction Codes (QECCs) are pivotal in advancing quantum computing by protecting quantum states against the adverse effects of noise and errors. With a variety of QECCs developed, including new developments and modifications of existing ones, selecting an appropriate QECC tailored to specific conditions is crucial. Despite significant improvements in the field of QECCs, a unified methodology for evaluating them on a consistent basis has remained elusive. Addressing this gap, this paper presents the first benchmarking framework for QECCs, introducing a set of universal parameters. By evaluating eight prominent QECCs, we propose a comprehensive suite of eight parameters for their analysis. Our methodology establishes a universal benchmarking approach and highlights the complexity of quantum error correction, indicating that the choice of a QECC depends on the unique requirements and limitations of each scenario. Furthermore, we develop a systematic strategy for selecting QECCs that adapts to the specific requirements of a given scenario, facilitating a tailored approach to quantum error correction. Additionally, we introduce a novel QECC recommendation tool that assesses the characteristics of a given scenario provided by the user, subsequently recommending a spectrum of QECCs from most to least suitable, along with the maximum achievable distance for each code. This tool is designed to be adaptable, allowing for the inclusion of new QECCs and the modification of their parameters with minimal effort, ensuring its relevance in the evolving landscape of quantum computing.

quant-ph↗

arXiv2024

A Comprehensive Cross-Model Framework for Benchmarking the Performance of Quantum Hamiltonian Simulations

Quantum Hamiltonian simulation is one of the most promising applications of quantum computing and forms the basis for many quantum algorithms. Benchmarking them is an important gauge of progress in quantum computing technology. We present a methodology and software framework to evaluate various facets of the performance of gate-based quantum computers on Trotterized quantum Hamiltonian evolution. We propose three distinct modes for benchmarking: (i) comparing simulation on a real device to that on a noiseless classical simulator, (ii) comparing simulation on a real device with exact diagonalization results, and (iii) using scalable mirror circuit techniques to assess hardware performance in scenarios beyond classical simulation methods. We demonstrate this framework on five Hamiltonian models from the HamLib library: the Fermi and Bose-Hubbard models, the transverse field Ising model, the Heisenberg model, and the Max3SAT problem. Experiments were conducted using Qiskit's Aer simulator, BlueQubit's CPU cluster and GPU simulators, and IBM's quantum hardware. Our framework, extendable to other Hamiltonians, provides comprehensive performance profiles that reveal hardware and algorithmic limitations and measure both fidelity and execution times, identifying crossover points where quantum hardware outperforms CPU/GPU simulators.

quant-ph↗

arXiv2024

Guardians of the Quantum GAN

Quantum Generative Adversarial Networks (qGANs) are at the forefront of image-generating quantum machine learning models. To accommodate the growing demand for Noisy Intermediate-Scale Quantum (NISQ) devices to train and infer quantum machine learning models, the number of third-party vendors offering quantum hardware as a service is expected to rise. This expansion introduces the risk of untrusted vendors potentially stealing proprietary information from the quantum machine learning models. To address this concern we propose a novel watermarking technique that exploits the noise signature embedded during the training phase of qGANs as a non-invasive watermark. The watermark is identifiable in the images generated by the qGAN allowing us to trace the specific quantum hardware used during training hence providing strong proof of ownership. To further enhance the security robustness, we propose the training of qGANs on a sequence of multiple quantum hardware, embedding a complex watermark comprising the noise signatures of all the training hardware that is difficult for adversaries to replicate. We also develop a machine learning classifier to extract this watermark robustly, thereby identifying the training hardware (or the suite of hardware) from the images generated by the qGAN validating the authenticity of the model. We note that the watermark signature is robust against inferencing on hardware different than the hardware that was used for training. We obtain watermark extraction accuracy of 100% and ~90% for training the qGAN on individual and multiple quantum hardware setups (and inferencing on different hardware), respectively. Since parameter evolution during training is strongly modulated by quantum noise, the proposed watermark can be extended to other quantum machine learning models as well.

quant-ph↗

arXiv2024

Investigating impact of bit-flip errors in control electronics on quantum computation

In this paper, we investigate the impact of bit flip errors in FPGA memories in control electronics on quantum computing systems. FPGA memories are integral in storing the amplitude and phase information pulse envelopes, which are essential for generating quantum gate pulses. However, these memories can incur faults due to physical and environmental stressors such as electromagnetic interference, power fluctuations, and temperature variations and adversarial fault injections, potentially leading to errors in quantum gate operations. To understand how these faults affect quantum computations, we conducted a series of experiments to introduce bit flips into the amplitude (both real and imaginary components) and phase values of quantum pulses using IBM's simulated quantum environments, FakeValencia, FakeManila, and FakeLima. Our findings reveal that bit flips in the exponent and initial mantissa bits of the real amplitude cause substantial deviations in quantum gate operations, with TVD increases as high as ~200%. Interestingly, the remaining bits exhibited natural tolerance to errors. We proposed a 3-bit repetition error correction code, which effectively reduced the TVD increases to below 40% without incurring any memory overhead. Due to reuse of less significant bits for error correction, the proposed approach introduces maximum of 5-7% extra TVD in nominal cases. However, this can be avoided by sacrificing memory area for implementing the repetition code.

quant-ph↗

arXiv2024

Lattice Surgery for Dummies

Quantum error correction (QEC) plays a crucial role in correcting noise and paving the way for fault-tolerant quantum computing. This field has seen significant advancements, with new quantum error correction codes emerging regularly to address errors effectively. Among these, topological codes, particularly surface codes, stand out for their low error thresholds and feasibility for implementation in large-scale quantum computers. However, these codes are restricted to encoding a single qubit. Lattice surgery is crucial for enabling interactions among multiple encoded qubits or between the lattices of a surface code, ensuring that its sophisticated error-correcting features are maintained without significantly increasing the operational overhead. Lattice surgery is pivotal for scaling QECCs across more extensive quantum systems. Despite its critical importance, comprehending lattice surgery is challenging due to its inherent complexity, demanding a deep understanding of intricate quantum physics and mathematical concepts. This paper endeavors to demystify lattice surgery, making it accessible to those without a profound background in quantum physics or mathematics. This work explores surface codes, introduces the basics of lattice surgery, and demonstrates its application in building quantum gates and emulating multi-qubit circuits.

quant-ph↗

arXiv2024

MITS: A Quantum Sorcerer Stone For Designing Surface Codes

In the evolving landscape of quantum computing, determining the most efficient parameters for Quantum Error Correction (QEC) is paramount. Various quantum computers possess varied types and amounts of physical noise. Traditionally, simulators operate in a forward paradigm, taking parameters such as distance, rounds, and physical error to output a logical error rate. However, usage of maximum distance and rounds of the surface code might waste resources. An approach that relies on trial and error to fine-tune QEC code parameters using simulation tools like STIM can be exceedingly time-consuming. Additionally, daily fluctuations in quantum error rates can alter the ideal QEC settings needed. As a result, there is a crucial need for an automated solution that can rapidly determine the appropriate QEC parameters tailored to the current conditions. To bridge this gap, we present MITS, a tool designed to reverse-engineer the well-known simulator STIM for designing QEC codes. MITS accepts the specific noise model of a quantum computer and a target logical error rate as input and outputs the optimal surface code rounds and code distances. This guarantees minimal qubit and gate usage, harmonizing the desired logical error rate with the existing hardware limitations on qubit numbers and gate fidelity. We explored and compared multiple heuristics and machine learning models for training/designing MITS and concluded that XGBoost and Random Forest regression were most effective, with Pearson correlation coefficients of 0.98 and 0.96 respectively.

quant-ph↗

arXiv2024

Q-Embroidery: A Study on Weaving Quantum Error Correction into the Fabric of Quantum Classifiers

Quantum computing holds transformative potential for various fields, yet its practical application is hindered by the susceptibility to errors. This study makes a pioneering contribution by applying quantum error correction codes (QECCs) for complex, multi-qubit classification tasks. We implement 1-qubit and 2-qubit quantum classifiers with QECCs, specifically the Steane code, and the distance 3 & 5 surface codes to analyze 2-dimensional and 4-dimensional datasets. This research uniquely evaluates the performance of these QECCs in enhancing the robustness and accuracy of quantum classifiers against various physical errors, including bit-flip, phase-flip, and depolarizing errors. The results emphasize that the effectiveness of a QECC in practical scenarios depends on various factors, including qubit availability, desired accuracy, and the specific types and levels of physical errors, rather than solely on theoretical superiority.

quant-ph↗