arXiv subjects

Mohan Sarovar

Publications and source records attributed to Mohan Sarovar.

At least 19 recordsLinked to original sources

arXiv2026

A heterogeneously integrated coupled-cavity frequency beam splitter

Frequency encoded photonic qubits promise a scalable path towards high-dimensional quantum information processing, but require efficient components for coherently mixing frequency modes. Coupled cavity modulators provide this functionality by using only a single driving microwave tone to couple hybridized optical supermodes. Here, we demonstrate a heterogeneously integrated thin-film lithium-niobate-on-silicon coupled-cavity modulator that realizes tunable bidirectional frequency mode transformations, including $50/50$ beam splitting and near complete frequency swapping with $>20~\mathrm{dB}$ pump extinction at a $10~\mathrm{GHz}$ supermode splitting. Because the electro-optic film is bonded onto a foundry fabricated silicon photonics platform, the approach is compatible with co-integration of photon pair sources, spectral filters, active tuning elements, and single photon detectors. We also bond thin-film lithium tantalate onto the same coupled-cavity platform, demonstrating material flexibility for scalable integrated frequency bin quantum photonic circuits.

quant-ph↗

arXiv2026

Enhancing the phase sensitivity of a Mach-Zehnder interferometer beyond the Heisenberg limit through dynamic squeezing

We propose a method to enhance the phase sensitivity of a Mach-Zehnder interferometer, independent of its input states. This is achieved by applying squeezing sequences to one arm of the interferometer. By alternating between squeezing along orthogonal quadratures, we demonstrate that the phase sensitivity of a Mach-Zehnder interferometer can be generically enhanced. Since this enhancement is independent of the input states, the proposed dynamic squeezing method allows for further improvement in phase sensitivity when combined with non-classical input states. We compare the dynamic squeezing approach with existing quantum sensing protocols and show that super-Heisenberg scaling can be achieved. Finally, we demonstrate that the scheme is robust against moderate photon loss.

quant-ph↗

arXiv2026

Limits of heralded photonic Bell-state generation in the presence of loss

High-quality entangled states of photons underlie quantum information science (QIS) applications across communication, sensing, and computing. In many discrete-variable photonic QIS architectures, large application-ready states (e.g., cluster states, repeater graph states) are constructed via fusion measurements on small entangled seed states, of which Bell states are the fundamental example. The quality of seed-state generation therefore sets a baseline for application performance, making it crucial to understand this process under realistic error mechanisms, particularly in integrated photonics experiments. In this work, we analyze five heralded schemes for generating event-ready photonic Bell states, contrasting their heralding probabilities, fidelities, and error robustness. We develop a hierarchy of error models, progressing from an analytically tractable lumped-loss model to realistic heralded single-photon sources with multiphoton emission errors and finally to integrated frequency-bin implementations with architecture-dependent loss. Across these models, we find that schemes based on higher-order multiphoton interference provide superior fidelity robustness in low-loss implementations, while lower-photon-number schemes can be preferable when probabilistic sources or lossy beamsplitters dominate the resource cost. Our results provide design guidance for integrated discrete-variable quantum photonics, with particular relevance for frequency-bin architectures where active beamsplitter loss can determine the optimal resource-state-generation strategy.

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arXiv2026

A Model Predictive Control-Inspired Quantum Algorithm

We introduce a new hybrid quantum-classical algorithm inspired by an advanced control strategy known as model predictive control (MPC). This algorithm unifies the optimization-based design of variational quantum algorithms (VQAs) with the feedback-based design of feedback-based quantum algorithms (FQAs). Variational circuit parameters are optimized using a layer-wise receding horizon strategy, where observable measurements after every layer initialize a classically simulated dynamic model used to predict quantum state evolution and optimize over future parameterized gates. This hybrid algorithm can be used for applications such as ground state preparation and approximate combinatorial optimization, and presents an ideal use case for the integration of quantum computers with high-performance computing, where the latter resource can be used to increase the scale and efficiency of the predictions critical to MPC. We show through mathematical proof and numerical evidence that the MPC-based algorithm can be guaranteed to at least match the performance of FQAs. Through simulations on Max-Cut problems and a two-dimensional transverse-field Ising model, we demonstrate that relaxed implementations of the MPC-based algorithm can also provide improved performance in practice compared to an FQA.

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arXiv2026

Learning Gaussian optical states with quantum computers

Recent results have established dramatic advantages in learning properties of quantum states when a quantum computer is available to process or jointly measure multiple copies of the unknown quantum state. Learning tasks can be accomplished with exponentially fewer copies of the state when compared to optimized classical learning strategies that are restricted to measuring one copy of the state at a time. While these results were established in abstract settings and for artificial learning tasks, they motivate the application of quantum computers to imaging and sensing of weak electromagnetic fields since these settings are ultimately concerned with the learning of unknown quantum states. In this work we apply these new results in quantum learning to the problem of learning Gaussian states of the electromagnetic field, which are germane since they describe most fields used in imaging and sensing. In order to connect with quantum learning theory, we consider the transduction of an $n$-mode Gaussian state into a register of qubits on a quantum computer followed by optimized measurements on these qubits to extract the parameters defining the original Gaussian state. We rigorously bound the number of copies of the Gaussian state required to achieve worst-case additive error in parameter estimates. The scaling of this bound with $n$ is exponentially better than na\"ive strategies for characterizing Gaussian states and matches recently derived bounds for characterization of Gaussian states using continuous-variable (CV) classical shadows. In addition, our bound has a polynomially better dependence on the energy of the multimode Gaussian state compared to the CV shadows protocol.

quant-ph↗

arXiv2026

Modeling integrated frequency shifters and beam splitters

Photonic quantum computing is a strong contender in the race to fault-tolerance. Recent proposals using qubits encoded in frequency modes promise a large reduction in hardware footprint, and have garnered much attention. In this encoding, linear optics, i.e., beam splitters and phase shifters, is necessarily not energy-conserving, and is costly to implement. In this work, we present designs of frequency-mode beam splitters based on modulated arrays of coupled resonators. We develop a methodology to construct their effective transfer matrices based on the SLH formalism for quantum input-output networks. Our methodology is flexible and highly composable, allowing us to define $N$-mode beam splitters either natively based on arrays of $N$-resonators of arbitrary connectivity or as networks of interconnected $l$-mode beam splitters, with $l<N$. We apply our methodology to analyze a two-resonator device, a frequency-domain phase shifter and a Mach-Zehnder interferometer obtained from composing these devices, a four-resonator device, and present a formal no-go theorem on the possibility of natively generating certain $N$-mode frequency-domain beam splitters with arrays of $N$-resonators.

quant-ph↗

arXiv2026

Quantum computational imaging and sensing

We present a new framework for imaging and sensing based on utilizing a quantum computer to coherently process quantum information in an electromagnetic field. We describe the framework, its potential to provide improvements in imaging and sensing performance and present an example application, the design of coherent receivers for optical communication. Finally, we go over the improvements in quantum technologies required to fully realize quantum computational imaging and sensing.

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arXiv2026

Data-driven learning of non-Markovian quantum dynamics

Fault-tolerant quantum computing requires extremely precise knowledge and control of qubit dynamics during the application of a gate. We develop a data-driven learning protocol for characterizing quantum gates that builds off previous work on learning the Nakajima-Mori-Zwanzig (NMZ) formulation of open system dynamics from time series data, which allows detailed reconstruction of quantum evolution, including non-Markovian dynamics. We demonstrate this learning technique on three different systems: a simulation of a qubit whose dynamics are purely Markovian, a simulation of a driven qubit coupled to stochastic noise produced by an Ornstein-Uhlenbeck process, and trapped-ion experimental data of a driven qubit whose noise environment is not characterized ahead of time. Our technique is able to learn the generators of time evolution, or the NMZ operators, in all three cases and can learn the timescale in which the qubit dynamics can no longer be accurately described by a purely Markovian model. Our technique complements existing quantum gate characterization methods such as gate set tomography by explicitly capturing non-Markovianity in the gate generator, thus allowing for more thorough diagnosis of noise sources.

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arXiv2025

Application Scale Quantum Circuit Compilation with Controlled Error

Compilation and optimization of quantum circuits are critical components in the execution of algorithms on quantum computers. These components must successfully balance two competing priorities: minimizing the number of expensive resources, such as two-qubit gates or arbitrary angle single-qubit rotations, and minimizing the approximation error of the compiled circuit to the ideal target unitary describing the quantum algorithm. We develop a practical workflow for managing and optimizing this tradeoff, which enables quantum circuit compilation and optimization at scales of hundreds of qubits. Our workflow is able to tackle circuits at such large scales while providing rigorous guarantees on circuit output error by leveraging circuit partitioning and the notion of averaging over circuit ensembles. We demonstrate our workflow on several benchmark algorithmic circuits acting on up to 380 qubits, and show that it can simultaneously achieve substantial reductions in resource-intensive gates and control output errors, offering a practical and scalable strategy for both near-term and fault-tolerant quantum computing.

quant-ph↗

arXiv2025

Real-time adaptation of quantum noise channel estimates

Estimates of noise channels for quantum gates are required for most error mitigation techniques and are desirable for informing quantum error correction decoders. These estimates can be obtained by resource-intensive off-line characterization techniques, but can become stale due to device drift and fluctuations. We propose a method to address this issue by performing real-time adaptation of noise channel estimates during the execution of a quantum algorithmic circuit using extended flag gadgets, mid-circuit measurements and Bayesian inference. We carry out analytical calculations and numerical simulations employing a Dirichlet prior distribution for the error rates in a Pauli channel to demonstrate and evaluate the technique, which can be seen as a protocol for real-time calibration of high-level gate error information.

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arXiv2023

Quantum Computing, Math, and Physics (QCaMP): Introducing quantum computing in high schools

The nascent but rapidly growing field of Quantum Information Science and Technology has led to an increased demand for skilled quantum workers and an opportunity to build a diverse workforce at the outset. In order to meet this demand and encourage women and underrepresented minorities in STEM to consider a career in QIST, we have developed a curriculum for introducing quantum computing to teachers and students at the high school level with no prerequisites. In 2022, this curriculum was delivered over the course of two one-week summer camps, one targeting teachers and another targeting students. Here, we present an overview of the objectives, curriculum, and activities, as well as results from the formal evaluation of both camps and the outlook for expanding QCaMP in future years.

physics.ed-ph↗

arXiv2023

Quantum computer-enabled receivers for optical communication

Optical communication is the standard for high-bandwidth information transfer in today's digital age. The increasing demand for bandwidth has led to the maturation of coherent transceivers that use phase- and amplitude-modulated optical signals to encode more bits of information per transmitted pulse. Such encoding schemes achieve higher information density, but also require more complicated receivers to discriminate the signaling states. In fact, achieving the ultimate limit of optical communication capacity, especially in the low light regime, requires coherent joint detection of multiple pulses. Despite their superiority, such joint detection receivers are not in widespread use because of the difficulty of constructing them in the optical domain. In this work we describe how optomechanical transduction of phase information from coherent optical pulses to superconducting qubit states followed by the execution of trained short-depth variational quantum circuits can perform joint detection of communication codewords with error probabilities that surpass all classical, individual pulse detection receivers. Importantly, we utilize a model of optomechanical transduction that captures non-idealities such as thermal noise and loss in order to understand the transduction performance necessary to achieve a quantum advantage with such a scheme. We also execute the trained variational circuits on an IBM-Q device with the modeled transduced states as input to demonstrate that a quantum advantage is possible even with current levels of quantum computing hardware noise.

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arXiv2022

Shining light on data: Geometric data analysis through quantum dynamics

Experimental sciences have come to depend heavily on our ability to organize and interpret high-dimensional datasets. Natural laws, conservation principles, and inter-dependencies among observed variables yield geometric structure, with fewer degrees of freedom, on the dataset. We introduce the frameworks of semiclassical and microlocal analysis to data analysis and develop a novel, yet natural uncertainty principle for extracting fine-scale features of this geometric structure in data, crucially dependent on data-driven approximations to quantum mechanical processes underlying geometric optics. This leads to the first tractable algorithm for approximation of wave dynamics and geodesics on data manifolds with rigorous probabilistic convergence rates under the manifold hypothesis. We demonstrate our algorithm on real-world datasets, including an analysis of population mobility information during the COVID-19 pandemic to achieve four-fold improvement in dimensionality reduction over existing state-of-the-art and reveal anomalous behavior exhibited by less than 1.2% of the entire dataset. Our work initiates the study of data-driven quantum dynamics for analyzing datasets, and we outline several future directions for research.

quant-ph↗

arXiv2022

Self-healing of Trotter error in digital adiabatic state preparation

Adiabatic time evolution can be used to prepare a complicated quantum many-body state from one that is easier to synthesize and Trotterization can be used to implement such an evolution digitally. The complex interplay between non-adiabaticity and digitization influences the infidelity of this process. We prove that the first-order Trotterization of a complete adiabatic evolution has a cumulative infidelity that scales as $\mathcal O(T^{-2} \delta t^2)$ instead of $\mathcal O(T^2 \delta t^2)$ expected from general Trotter error bounds, where $\delta t$ is the time step and $T$ is the total time. This result suggests a self-healing mechanism and explains why, despite increasing $T$, infidelities for fixed-$\delta t$ digitized evolutions still decrease for a wide variety of Hamiltonians. It also establishes a correspondence between the Quantum Approximate Optimization Algorithm (QAOA) and digitized quantum annealing.

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arXiv2022

Nanoscale Architecture for Frequency-Resolving Single-Photon Detectors

Single photon detectors play a key role across several basic science and technology applications. While progress has been made in improving performance, single photon detectors that can maintain high performance while also resolving the photon frequency are still lacking. By means of quantum simulations, we show that nanoscale elements cooperatively interacting with the photon field in a photodetector architecture allow to simultaneously achieve high efficiency, low jitter, and high frequency resolution. We discuss how such cooperative interactions are essential to reach this performance regime, analyzing the factors that impact performance and trade-offs between metrics. We illustrate the potential performance for frequency resolution over a 1 eV bandwidth in the visible range, indicating near perfect detection efficiency, jitter of a few hundred femtoseconds, and frequency resolution of tens of meV. Finally, a potential physical realization of such an architecture is presented based on carbon nanotubes functionalized with quantum dots.

quant-ph↗

arXiv2022

Establishing trust in quantum computations

Quantum computing hardware has grown sufficiently complex that it often can no longer be simulated by classical computers, but its computational power remains limited by errors. These errors corrupt the results of quantum algorithms, and it is no longer always feasible to use classical simulations to directly check the correctness of quantum computations. Without practical methods for quantifying the accuracy with which a quantum algorithm has been executed, it is difficult to establish trust in the results of a quantum computation. Here we solve this problem, by introducing a simple and efficient technique for measuring the fidelity with which an as-built quantum computer can execute an algorithm. Our technique converts the algorithm's quantum circuits into a set of closely related ``mirror circuits'' whose success rates can be efficiently measured. It enables measuring the fidelity of quantum algorithm executions both in the near-term, with algorithms run on hundreds or thousands of physical qubits, and into the future, with algorithms run on logical qubits protected by quantum error correction.

quant-ph↗

arXiv2022

Surrogate-based optimization for variational quantum algorithms

Variational quantum algorithms are a class of techniques intended to be used on near-term quantum computers. The goal of these algorithms is to perform large quantum computations by breaking the problem down into a large number of shallow quantum circuits, complemented by classical optimization and feedback between each circuit execution. One path for improving the performance of these algorithms is to enhance the classical optimization technique. Given the relative ease and abundance of classical computing resources, there is ample opportunity to do so. In this work, we introduce the idea of learning surrogate models for variational circuits using few experimental measurements, and then performing parameter optimization using these models as opposed to the original data. We demonstrate this idea using a surrogate model based on kernel approximations, through which we reconstruct local patches of variational cost functions using batches of noisy quantum circuit results. Through application to the quantum approximate optimization algorithm and preparation of ground states for molecules, we demonstrate the superiority of surrogate-based optimization over commonly-used optimization techniques for variational algorithms.

quant-ph↗

arXiv2022

Quantum circuit debugging and sensitivity analysis via local inversions

As the width and depth of quantum circuits implemented by state-of-the-art quantum processors rapidly increase, circuit analysis and assessment via classical simulation are becoming unfeasible. It is crucial, therefore, to develop new methods to identify significant error sources in large and complex quantum circuits. In this work, we present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most and thus helps to identify the most significant sources of error. The technique requires no classical verification of the circuit output and is thus a scalable tool for debugging large quantum programs in the form of circuits. We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.

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