arXiv subjects

Sophia E. Economou

Publications and source records attributed to Sophia E. Economou.

At least 37 records · Page 2Linked to original sources

arXiv2025

Reducing the Resources Required by ADAPT-VQE Using Coupled Exchange Operators and Improved Subroutines

Adaptive variational quantum algorithms arguably offer the best prospects for quantum advantage in the Noisy Intermediate-Scale Quantum era. Since the inception of the first such algorithm, the Adaptive Derivative-Assembled Problem-Tailored Variational Quantum Eigensolver (ADAPT-VQE), many improvements have appeared in the literature. We combine the key improvements along with a novel operator pool -- which we term Coupled Exchange Operator (CEO) pool -- to assess the cost of running state-of-the-art ADAPT-VQE on hardware in terms of measurement counts and circuit depth. We show a dramatic reduction of these quantum computational resources compared to the early versions of the algorithm: CNOT count, CNOT depth and measurement costs are reduced by up to 88%, 96% and 99.6%, respectively, for molecules represented by 12 to 14 qubits (LiH, H6 and BeH2). We also find that our state-of-the-art CEO-ADAPT-VQE outperforms the Unitary Coupled Cluster Singles and Doubles ansatz, the most widely used static VQE ansatz, in all relevant metrics, and offers a five order of magnitude decrease in measurement costs as compared to other static ansätze with competitive CNOT counts.

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arXiv2025

Qubit-efficient quantum chemistry with the ADAPT variational quantum eigensolver and double unitary downfolding

In this work, we combine the recently developed double unitary coupled cluster (DUCC) theory with the adaptive, problem-tailored variational quantum eigensolver (ADAPT-VQE) to explore accuracy of unitary downfolded Hamiltonians for quantum simulation of chemistry. We benchmark the ability of DUCC effective Hamiltonians to recover dynamical correlation energy outside of an active space. We consider the effects of strong correlation, commutator truncation, higher-body terms, and approximate external amplitudes on the accuracy of these effective Hamiltonians. When combining these DUCC Hamiltonians with ADAPT-VQE, we observe similar convergence of the ground state as compared to bare active space Hamiltonians, demonstrating that DUCC Hamiltonians provide increased accuracy without increasing the load on the quantum processor.

quant-ph↗

arXiv2025

Implementing and benchmarking dynamically corrected gates on superconducting devices using space curve quantum control

We use Space Curve Quantum Control (SCQC) to design, experimentally demonstrate, and benchmark dynamically corrected single-qubit gates on IBM hardware, comparing their performance to that of the standard gates provided by IBM. Our gates are designed to dynamically suppress both detuning and pulse-amplitude noise, with gate times as short as 88 ns. We compare our gates against those of IBM on two separate IBM devices and across sets of up to 18 qubits. Randomized benchmarking is done utilizing our detuning- and amplitude-robust gates in randomized Clifford circuits containing up to 4000 gates. Our gates achieve error-per-Clifford rates that reach as low as 7$\times10^{-5}$ ($\pm10^{-6}$) and which remain nearly constant as the compound noise is increased up to 4% amplitude noise and up to a detuning noise of 342 kHz; this is in contrast to the IBM gates, which exhibit rates that drop to order $10^{-3}$ across this range. This range is consistent with the commonly reported frequency fluctuations and with the upper bound of the statistical uncertainty in gate calibration. In addition, we investigate the performance across larger noise ranges of up to 20% amplitude and 3.5 MHz detuning noise using quantum process tomography. Finally, we experimentally demonstrate how SCQC can be tailored to different practical use cases by trading off amplitude-robustness for ultrafast 60 ns dephasing-only robust pulses. Our work establishes experimental guidelines for implementing SCQC-designed dynamically corrected gates on a broad range of qubit hardware to limit the effect of noise-induced errors and decoherence.

quant-ph↗

arXiv2024

Reducing measurement costs by recycling the Hessian in adaptive variational quantum algorithms

Adaptive protocols enable the construction of more efficient state preparation circuits in variational quantum algorithms (VQAs) by utilizing data obtained from the quantum processor during the execution of the algorithm. This idea originated with ADAPT-VQE, an algorithm that iteratively grows the state preparation circuit operator by operator, with each new operator accompanied by a new variational parameter, and where all parameters acquired thus far are optimized in each iteration. In ADAPT-VQE and other adaptive VQAs that followed it, it has been shown that initializing parameters to their optimal values from the previous iteration speeds up convergence and avoids shallow local traps in the parameter landscape. However, no other data from the optimization performed at one iteration is carried over to the next. In this work, we propose an improved quasi-Newton optimization protocol specifically tailored to adaptive VQAs. The distinctive feature in our proposal is that approximate second derivatives of the cost function are recycled across iterations in addition to parameter values. We implement a quasi-Newton optimizer where an approximation to the inverse Hessian matrix is continuously built and grown across the iterations of an adaptive VQA. The resulting algorithm has the flavor of a continuous optimization where the dimension of the search space is augmented when the gradient norm falls below a given threshold. We show that this inter-optimization exchange of second-order information leads the Hessian in the state of the optimizer to better approximate the exact Hessian. As a result, our method achieves a superlinear convergence rate even in situations where the typical quasi-Newton optimizer converges only linearly. Our protocol decreases the measurement costs in implementing adaptive VQAs on quantum hardware as well as the runtime of their classical simulation.

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arXiv2024

Non-Variational ADAPT algorithm for quantum simulations

We explore a non-variational quantum state preparation approach combined with the ADAPT operator selection strategy in the application of preparing the ground state of a desired target Hamiltonian. In this algorithm, energy gradient measurements determine both the operators and the gate parameters in the quantum circuit construction. We compare this non-variational algorithm with ADAPT-VQE and with feedback-based quantum algorithms in terms of the rate of energy reduction, the circuit depth, and the measurement cost in molecular simulation. We find that despite using deeper circuits, this new algorithm reaches chemical accuracy at a similar measurement cost to ADAPT-VQE. Since it does not rely on a classical optimization subroutine, it may provide robustness against circuit parameter errors due to imperfect control or gate synthesis.

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arXiv2024

Classical Pre-optimization Approach for ADAPT-VQE: Maximizing the Potential of High-Performance Computing Resources to Improve Quantum Simulation of Chemical Applications

The ADAPT-VQE algorithm is a promising method for generating a compact ansatz based on derivatives of the underlying cost function, and it yields accurate predictions of electronic energies for molecules. In this work we report the implementation and performance of ADAPT-VQE with our recently developed sparse wavefunction circuit solver (SWCS) in terms of accuracy and efficiency for molecular systems with up to 52 spin-orbitals. The SWCS can be tuned to balance computational cost and accuracy, which extends the application of ADAPT-VQE for molecular electronic structure calculations to larger basis sets and larger number of qubits. Using this tunable feature of the SWCS, we propose an alternative optimization procedure for ADAPT-VQE to reduce the computational cost of the optimization. By pre-optimizing a quantum simulation with a parameterized ansatz generated with ADAPT-VQE/SWCS, we aim to utilize the power of classical high-performance computing in order to minimize the work required on noisy intermediate-scale quantum hardware, which offers a promising path toward demonstrating quantum advantage for chemical applications.

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arXiv2024

Outcomes from a Workshop on a National Center for Quantum Education

In response to numerous programs seeking to advance quantum education and workforce development in the United States, experts from academia, industry, government, and professional societies convened for a National Science Foundation-sponsored workshop in February 2024 to explore the benefits and challenges of establishing a national center for quantum education. Broadly, such a center would foster collaboration and build the infrastructure required to develop a diverse and quantum-ready workforce. The workshop discussions centered around how a center could uniquely address gaps in public, K-12, and undergraduate quantum information science and engineering (QISE) education. Specifically, the community identified activities that, through a center, could lead to an increase in student awareness of quantum careers, boost the number of educators trained in quantum-related subjects, strengthen pathways into quantum careers, enhance the understanding of the U.S. quantum workforce, and elevate public engagement with QISE. Core proposed activities for the center include professional development for educators, coordinated curriculum development and curation, expanded access to educational laboratory equipment, robust evaluation and assessment practices, network building, and enhanced public engagement with quantum science.

physics.ed-ph↗

arXiv2024

Surrogate Constructed Scalable Circuits ADAPT-VQE in the Schwinger model

Inspired by recent advancements of simulating periodic systems on quantum computers, we develop a new approach, (SC)$^2$-ADAPT-VQE, to further advance the simulation of these systems. Our approach extends the scalable circuits ADAPT-VQE framework, which builds an ansatz from a pool of coordinate-invariant operators defined for arbitrarily large, though not arbitrarily small, volumes. Our method uses a classically tractable ``Surrogate Constructed'' method to remove irrelevant operators from the pool, reducing the minimum size for which the scalable circuits are defined. Bringing together the scalable circuits and the surrogate constructed approaches forms the core of the (SC)$^2$ methodology. Our approach allows for a wider set of classical computations, on small volumes, which can be used for a more robust extrapolation protocol. While developed in the context of lattice models, the surrogate construction portion is applicable to a wide variety of problems where information about the relative importance of operators in the pool is available. As an example, we use it to compute properties of the Schwinger model - quantum electrodynamics for a single, massive fermion in $1+1$ dimensions - and show that our method can be used to accurately extrapolate to the continuum limit.

quant-ph↗

arXiv2024

Adaptive Quantum Generative Training using an Unbounded Loss Function

We propose a generative quantum learning algorithm, Rényi-ADAPT, using the Adaptive Derivative-Assembled Problem Tailored ansatz (ADAPT) framework in which the loss function to be minimized is the maximal quantum Rényi divergence of order two, an unbounded function that mitigates barren plateaus which inhibit training variational circuits. We benchmark this method against other state-of-the-art adaptive algorithms by learning random two-local thermal states. We perform numerical experiments on systems of up to 12 qubits, comparing our method to learning algorithms that use linear objective functions, and show that Rényi-ADAPT is capable of constructing shallow quantum circuits competitive with existing methods, while the gradients remain favorable resulting from the maximal Rényi divergence loss function.

quant-ph↗

arXiv2024

Optimization complexity and resource minimization of emitter-based photonic graph state generation protocols

Photonic graph states are important for measurement- and fusion-based quantum computing, quantum networks, and sensing. They can in principle be generated deterministically by using emitters to create the requisite entanglement. Finding ways to minimize the number of entangling gates between emitters and understanding the overall optimization complexity of such protocols is crucial for practical implementations. Here, we address these issues using graph theory concepts. We develop optimizers that minimize the number of entangling gates, reducing them by up to 75$\%$ compared to naive schemes for moderately sized random graphs. While the complexity of optimizing emitter-emitter CNOT counts is likely NP-hard, we are able to develop heuristics based on strong connections between graph transformations and the optimization of stabilizer circuits. These patterns allow us to process large graphs and still achieve a reduction of up to $66\%$ in emitter CNOTs, without relying on subtle metrics such as edge density. We find the optimal emission orderings and circuits to prepare unencoded and encoded repeater graph states of any size, achieving global minimization of emitter and CNOT resources despite the average NP-hardness of both optimization problems. We further study the locally equivalent orbit of graphs. Although enumerating orbits is $\#$P complete for arbitrary graphs, we analytically calculate the size of the orbit of repeater graphs and find a procedure to generate the orbit for any repeater size. Finally, we inspect the entangling gate cost of preparing any graph from a given orbit and show that we can achieve the same optimal CNOT count across the orbit.

quant-ph↗

arXiv2024

Minimal evolution times for fast, pulse-based state preparation in silicon spin qubits

Standing as one of the most significant barriers to reaching quantum advantage, state-preparation fidelities on noisy intermediate-scale quantum processors suffer from quantum-gate errors, which accumulate over time. A potential remedy is pulse-based state preparation. We numerically investigate the minimal evolution times (METs) attainable by optimizing (microwave and exchange) pulses on silicon hardware. We investigate two state preparation tasks. First, we consider the preparation of molecular ground states and find the METs for H$_2$, HeH$^+$, and LiH to be 2.4 ns, 4.4 ns, and 27.2 ns, respectively. Second, we consider transitions between arbitrary states and find the METs for transitions between arbitrary four-qubit states to be below 50 ns. For comparison, connecting arbitrary two-qubit states via one- and two-qubit gates on the same silicon processor requires approximately 200 ns. This comparison indicates that pulse-based state preparation is likely to utilize the coherence times of silicon hardware more efficiently than gate-based state preparation. Finally, we quantify the effect of silicon device parameters on the MET. We show that increasing the maximal exchange amplitude from 10 MHz to 1 GHz accelerates the METs, e.g., for H$_2$ from 84.3 ns to 2.4 ns. This demonstrates the importance of fast exchange. We also show that increasing the maximal amplitude of the microwave drive from 884 kHz to 56.6 MHz shortens state transitions, e.g., for two-qubit states from 1000 ns to 25 ns. Our results bound both the state-preparation times for general quantum algorithms and the execution times of variational quantum algorithms with silicon spin qubits.

quant-ph↗

arXiv2024

High-throughput assessment of defect-nuclear spin register controllability for quantum memory applications

Quantum memories play a key role in facilitating tasks within quantum networks and quantum information processing, including secure communications, advanced quantum sensing, and distributed quantum computing. Progress in characterizing large nuclear spin registers coupled to defect electronic spins has been significant, but selecting memory qubits remains challenging due to the multitude of possible assignments. Numerical simulations for evaluating entangling gate fidelities encounter obstacles, restricting research to small registers, while experimental investigations are time-consuming and often limited to well-understood samples. Here we present an efficient methodology for systematically assessing the controllability of defect systems coupled to nuclear spin registers. We showcase the approach by investigating the generation of entanglement links between defects in SiC and randomly selected sets of nuclear spins within the two-species ($^{13}$C and $^{29}$Si) nuclear register. We quantify the performance of entangling gate operations and present the achievable gate fidelities, considering both the size of the register and the presence of unwanted nuclear spins. We find that some control sequences perform better than others depending on the number of target versus bath nuclei. This efficient approach is a guide for both experimental investigation and engineering, facilitating the high-throughput exploration of suitable defect systems for quantum memories.

quant-ph↗

arXiv2024

Generation of genuine all-way entanglement in defect-nuclear spin systems through dynamical decoupling sequences

Multipartite entangled states are an essential resource for sensing, quantum error correction, and cryptography. Color centers in solids are one of the leading platforms for quantum networking due to the availability of a nuclear spin memory that can be entangled with the optically active electronic spin through dynamical decoupling sequences. Creating electron-nuclear entangled states in these systems is a difficult task as the always-on hyperfine interactions prohibit complete isolation of the target dynamics from the unwanted spin bath. While this emergent cross-talk can be alleviated by prolonging the entanglement generation, the gate durations quickly exceed coherence times. Here we show how to prepare high-quality GHZ$_M$-like states with minimal cross-talk. We introduce the $M$-tangling power of an evolution operator, which allows us to verify genuine all-way correlations. Using experimentally measured hyperfine parameters of an NV center spin in diamond coupled to carbon-13 lattice spins, we show how to use sequential or single-shot entangling operations to prepare GHZ$_M$-like states of up to $M=10$ qubits within time constraints that saturate bounds on $M$-way correlations. We study the entanglement of mixed electron-nuclear states and develop a non-unitary $M$-tangling power which additionally captures correlations arising from all unwanted nuclear spins. We further derive a non-unitary $M$-tangling power which incorporates the impact of electronic dephasing errors on the $M$-way correlations. Finally, we inspect the performance of our protocols in the presence of experimentally reported pulse errors, finding that XY decoupling sequences can lead to high-fidelity GHZ state preparation.

quant-ph↗

arXiv2024

Deterministic generation of qudit photonic graph states from quantum emitters

We propose and analyze deterministic protocols to generate qudit photonic graph states from quantum emitters. We show that our approach can be applied to generate any qudit graph state, and we exemplify it by constructing protocols to generate one- and two-dimensional qudit cluster states, absolutely maximally entangled states, and logical states of quantum error correcting codes. Some of these protocols make use of time-delayed feedback, while others do not. The only additional resource requirement compared to the qubit case is the ability to control multi-level emitters. These results significantly broaden the range of multi-photon entangled states that can be produced deterministically from quantum emitters.

quant-ph↗

arXiv2023

Scaling adaptive quantum simulation algorithms via operator pool tiling

Adaptive variational quantum simulation algorithms use information from the quantum computer to dynamically create optimal trial wavefunctions for a given problem Hamiltonian. A key ingredient in these algorithms is a predefined operator pool from which trial wavefunctions are constructed. Finding suitable pools is critical for the efficiency of the algorithm as the problem size increases. Here, we present a technique called operator pool tiling that facilitates the construction of problem-tailored pools for arbitrarily large problem instances. By first performing an ADAPT-VQE calculation on a smaller instance of the problem using a large, but computationally inefficient operator pool, we extract the most relevant operators and use them to design more efficient pools for larger instances. We demonstrate the method here on strongly correlated quantum spin models in one and two dimensions, finding that ADAPT automatically finds a highly effective ansatz for these systems. Given that many problems, such as those arising in condensed matter physics, have a naturally repeating lattice structure, we expect the pool tiling method to be a widely applicable technique apt for such systems.

quant-ph↗

arXiv2023

ADAPT-QAOA with a classically inspired initial state

Quantum computing may provide advantage in solving classical optimization problems. One promising algorithm is the quantum approximate optimization algorithm (QAOA). There have been many proposals for improving this algorithm, such as using an initial state informed by classical approximation solutions. A variation of QAOA called ADAPT-QAOA constructs the ansatz dynamically and can speed up convergence. However, it faces the challenge of frequently converging to excited states which correspond to local minima in the energy landscape, limiting its performance. In this work, we propose to start ADAPT-QAOA with an initial state inspired by a classical approximation algorithm. Through numerical simulations we show that this new algorithm can reach the same accuracy with fewer layers than the standard QAOA and the original ADAPT-QAOA. It also appears to be less prone to the problem of converging to excited states.

quant-ph↗

arXiv2023

Time-crystalline behavior in central-spin models with Heisenberg interactions

Time-crystalline behavior has been predicted and observed in quantum central-spin systems with periodic driving and Ising interactions. Here, we theoretically show that it can also arise in central-spin systems with Heisenberg interactions. We present two methods to achieve this: application of a sufficiently large Zeeman splitting on the central spin compared to the satellite spins, or else by applying additional pulses to the central spin every Floquet period. In both cases, we show that the system exhibits a subharmonic response in spin magnetizations in the presence of disorder for both pure Heisenberg and XXZ interactions. Our results pertain to any XXZ central-spin system, including hyperfine-coupled electron-nuclear systems in quantum dots or color centers.

quant-ph↗

arXiv2023

Randomized adaptive quantum state preparation

We develop an adaptive method for quantum state preparation that utilizes randomness as an essential component and that does not require classical optimization. Instead, a cost function is minimized to prepare a desired quantum state through an adaptively constructed quantum circuit, where each adaptive step is informed by feedback from gradient measurements in which the associated tangent space directions are randomized. We provide theoretical arguments and numerical evidence that convergence to the target state can be achieved for almost all initial states. We investigate different randomization procedures and develop lower bounds on the expected cost function change, which allows for drawing connections to barren plateaus and for assessing the applicability of the algorithm to large-scale problems.

quant-ph↗