Generation of High-Resolution Handwritten Digits with an Ion-Trap Quantum Computer

Rudolph, Manuel S.; Toussaint, Ntwali Bashige; Katabarwa, Amara; Johri, Sonika; Peropadre, Borja; Perdomo-Ortiz, Alejandro

doi:10.1103/physrevx.12.031010

Cited by 46 publications

(22 citation statements)

References 36 publications

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“…Extensive studies to elucidate the viability of quantum advantage in near-term quantum computing have produced mixed results so far [22,[25][26][27][28][29][30]. A recent analysis argued that VQE will fail to provide quantum advantage over state-of-the-art quantum chemistry algorithms for estimating the ground-state energy of an industry-scale problem Hamiltonian [31].…”

Section: Introductionmentioning

confidence: 99%

Noise tailoring for robust amplitude estimation

Dalal

Katabarwa²

2023

New J. Phys.

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A universal fault-tolerant quantum computer holds the promise to speed up computational problems that are otherwise intractable on classical computers; however, for the next decade or so, our access is restricted to noisy intermediate-scale quantum (NISQ) computers and, perhaps, early fault tolerant (EFT) quantum computers. This motivates the development of many near-term quantum algorithms including robust amplitude estimation (RAE), which is a quantum-enhanced algorithm for estimating expectation values. One obstacle to using RAE has been a paucity of ways of getting realistic error models incorporated into this algorithm. So far the impact of device noise on RAE is incorporated into one of its subroutines as an exponential decay model, which is unrealistic for NISQ devices and, maybe, for EFT devices; this hinders the performance of RAE. Rather than trying to explicitly model realistic noise effects, which may be infeasible, we circumvent this obstacle by tailoring device noise using randomized compiling to generate an effective noise model, whose impact on RAE closely resembles that of the exponential decay model. Using noisy simulations, we show that our noise-tailored RAE algorithm is able to regain improvements in both bias and precision that are expected for RAE. Additionally, on IBM's quantum computer ibmq_belem our algorithm demonstrates advantage over the standard estimation technique in reducing bias. Thus, our work extends the feasibility of RAE on NISQ computers, consequently bringing us one step closer towards achieving quantum advantage using these devices.

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Section: Introductionmentioning

confidence: 99%

Noise tailoring for robust amplitude estimation

Dalal

Katabarwa²

2023

New J. Phys.

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“…Nonetheless, it is a range in which it should be possible to do computations that cannot efficiently be simulated on a classical computer. Since its inception there has been a burst of research looking for a quantum advantage in different areas like quantum machine learning [2,3,4,5,6,7,8,9], quantum chemistry [10,11,12,13,14,15], and quantum finance [16,17,18]. An excellent and comprehensive view of near-term quantum algorithms is contained here [19].…”

Section: Introductionmentioning

confidence: 99%

Hypothesis Testing for Error Mitigation: How to Evaluate Error Mitigation

Saki¹,

Katabarwa²,

Resch³

et al. 2023

Preprint

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In the noisy intermediate-scale quantum (NISQ) era, quantum error mitigation will be a necessary tool to extract useful performance out of quantum devices. However, there is a big gap between the noise models often assumed by error mitigation techniques and the actual noise on quantum devices. As a consequence, there arises a gap between the theoretical expectations of the techniques and their everyday performance. Cloud users of quantum devices in particular, who often take the devices as they are, feel this gap the most. How should they parametrize their uncertainty in the usefulness of these techniques and be able to make judgement calls between resources required to implement error mitigation and the accuracy required at the algorithmic level? To answer the first question, we introduce hypothesis testing within the framework of quantum error mitigation and for the second question, we propose an inclusive figure of merit that accounts for both resource requirement and mitigation efficiency of an error mitigation implementation. The figure of merit is useful to weigh the trade-offs between the scalability and accuracy of various error mitigation methods. Finally, using the hypothesis testing and the figure of merit, we experimentally evaluate 16 error mitigation pipelines composed of singular methods such as zero noise extrapolation, randomized compilation, measurement error mitigation, dynamical decoupling, and mitigation with estimation circuits. In total our data involved running 275, 640 circuits on two IBM quantum computers.

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“…Despite the intrinsic theoretical advantages of quantum computers, the widespread adoption of quantum technologies will ultimately depend on the benefits they can offer for solving problems of high practical interest using these limited resources. To this end, parametrized quantum circuits (PQCs) [4][5][6] have been proposed as a promising formalism for leveraging nearterm quantum devices for the solution of problems in quantum chemistry [7][8][9], materials science [10], and quantum machine learning [11][12][13][14][15][16][17][18][19] applications which are difficult for classical algorithms.…”

Section: Introductionmentioning

confidence: 99%

Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage

Rudolph¹,

Miller

Chen

et al. 2022

Preprint

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While recent breakthroughs have proven the ability of noisy intermediate-scale quantum (NISQ) devices to achieve quantum advantage in classically-intractable sampling tasks, the use of these devices for solving more practically relevant computational problems remains a challenge. Proposals for attaining practical quantum advantage typically involve parametrized quantum circuits (PQCs), whose parameters can be optimized to find solutions to diverse problems throughout quantum simulation and machine learning. However, training PQCs for real-world problems remains a significant practical challenge, largely due to the phenomenon of barren plateaus in the optimization landscapes of randomly-initialized quantum circuits. In this work, we introduce a scalable procedure for harnessing classical computing resources to determine task-specific initializations of PQCs, which we show significantly improves the trainability and performance of PQCs on a variety of problems. Given a specific optimization task, this method first utilizes tensor network (TN) simulations to identify a promising quantum state, which is then converted into gate parameters of a PQC by means of a high-performance decomposition procedure. We show that this task-specific initialization avoids barren plateaus, and effectively translates increases in classical resources to enhanced performance and speed in training quantum circuits. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing, and opens up new avenues to harness the power of modern quantum hardware for realizing practical quantum advantage.

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Generation of High-Resolution Handwritten Digits with an Ion-Trap Quantum Computer

Cited by 46 publications

References 36 publications

Noise tailoring for robust amplitude estimation

Noise tailoring for robust amplitude estimation

Hypothesis Testing for Error Mitigation: How to Evaluate Error Mitigation

Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage

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