arXiv subjects

Lior Gazit

Publications and source records attributed to Lior Gazit.

7 recordsLinked to original sources

arXiv2026

Low-cost quantum error mitigation via auxiliary qubit return validation

We introduce a low-overhead technique for quantum error mitigation based on post-selection using auxiliary qubit measurements. The method exploits the structural property that, in an error-free computation, auxiliary qubits are often expected to return to the zero state after use. By selectively measuring these qubits at carefully chosen points in the circuit, erroneous shots can be identified and discarded, improving result fidelity with minimal hardware overhead. To account for circuit noise, including measurement errors, we analyze the likelihood that a measurement outcome indicates a corrupted shot. This analysis is informed by the measurement's backward light cone, namely the set of circuit operations that could affect the outcome. Shots whose auxiliary measurement outcomes imply a corruption likelihood above a tunable threshold are rejected. Simulations show that the method reduces the false-negative rate by approximately 10% while discarding only approximately 1% of valid shots. The threshold controls the bias-variance tradeoff inherent to post-selection, allowing the method to be adapted to the fidelity and sampling requirements of different applications.

quant-ph↗

arXiv2026

Optimal Approximation of Single Qubit Rotations within a Quantum Circuit

Fault-tolerant quantum computing typically requires the transpilation of arbitrary quantum circuits into a finite, universal gate set, such as Clifford+T. As a baseline, Diagonal approximation can be used for synthesizing single-qubit Pauli rotations, yielding an approximating sequence with $T$-count that equals $3 \log_2(1/\epsilon)$ for a target precision $\epsilon$. Magnitude Approximation can reduce the $T$-count to only $1 \log_2(1/\epsilon)$ by allowing large residual errors, which are rotations about orthogonal axes. Within a complete quantum circuit, these residual errors can then be absorbed into neighboring gates before they are approximated themselves. Determining the optimal allocation of approximation strategies within a large, multi-qubit circuit presents a significant combinatorial challenge. In this work, we present a linear-time algorithm that guarantees an optimal solution to this problem. We demonstrate that the issue of delegating Magnitude versus Diagonal approximation across a circuit maps formally to a classical 1D Ising model with a spatially varying field. By minimizing the energy of this Hamiltonian, we identify the optimal approximation configuration for each rotation without exponential overhead. Benchmarking our method against standard diagonal approximation on random quantum circuits, we observe an average reduction of 26\% in the total approximating circuit gate count, offering a significant efficiency gain for the implementation of quantum algorithms on near-term and fault-tolerant architectures.

quant-ph↗

arXiv2025

Qmod: Expressive High-Level Quantum Modeling

Quantum computing hardware is advancing at a rapid pace, yet the lack of high-level programming abstractions remains a serious bottleneck in the development of new applications. Widely used frameworks still rely on gate-level circuit descriptions, causing the algorithm's functional intent to become lost in low-level implementation details, and hindering flexibility and reuse. While various high-level quantum programming languages have emerged in recent years - offering a significant step toward higher abstraction - many still lack support for classical-like expression syntax, and native constructs for useful quantum algorithmic idioms. This paper presents Qmod, a high-level quantum programming language designed to capture algorithmic intent in natural terms while delegating implementation decisions to automation. Qmod introduces quantum numeric variables and expressions, including digital fixed-point arithmetic tuned for compact representations and optimal resource usage. Beyond digital encoding, Qmod also supports non-digital expression modes - phase and amplitude encoding - frequently exploited by quantum algorithms to achieve computational advantages. We describe the language's constructs, demonstrate practical usage examples, and outline future work on evaluating Qmod across a broader set of use cases.

quant-ph↗

arXiv2024

Design and synthesis of scalable quantum programs

We present a scalable, robust approach to creating quantum programs of arbitrary size and complexity. The approach is based on the true abstraction of the problem. The quantum program is expressed in terms of a high-level model together with constraints and objectives on the final program. Advanced synthesis algorithms transform the model into a low-level quantum program that meets the user's specification and is directed at a stipulated hardware. This separation of description from implementation is essential for scale. The technology adapts electronic design automation methods to quantum computing, finding feasible implementations in a virtually unlimited functional space. The results show clear superiority over the compilation and transpilation methods used today. We expect that this technological approach will take over and prevail as quantum software become more demanding, complex, and essential.

quant-ph↗

arXiv2022

Leveraging Time Irreversibility with Order-Contrastive Pre-training

Label-scarce, high-dimensional domains such as healthcare present a challenge for modern machine learning techniques. To overcome the difficulties posed by a lack of labeled data, we explore an "order-contrastive" method for self-supervised pre-training on longitudinal data. We sample pairs of time segments, switch the order for half of them, and train a model to predict whether a given pair is in the correct order. Intuitively, the ordering task allows the model to attend to the least time-reversible features (for example, features that indicate progression of a chronic disease). The same features are often useful for downstream tasks of interest. To quantify this, we study a simple theoretical setting where we prove a finite-sample guarantee for the downstream error of a representation learned with order-contrastive pre-training. Empirically, in synthetic and longitudinal healthcare settings, we demonstrate the effectiveness of order-contrastive pre-training in the small-data regime over supervised learning and other self-supervised pre-training baselines. Our results indicate that pre-training methods designed for particular classes of distributions and downstream tasks can improve the performance of self-supervised learning.

cs.LG↗

arXiv2021

A trapped ion quantum computer with robust entangling gates and quantum coherent feedback

Quantum computers are expected to achieve a significant speed-up over classical computers in solving a range of computational problems. Chains of ions held in a linear Paul trap are a promising platform for constructing such quantum computers, due to their long coherence times and high quality of control. Here we report on the construction of a small, five-qubit, universal quantum computer using $^{88}\text{Sr}^{+}$ ions in an RF trap. All basic operations, including initialization, quantum logic operations, and readout, are performed with high fidelity. Selective two-qubit and single-qubit gates, implemented using a narrow linewidth laser, comprise a universal gate set, allowing realization of any unitary on the quantum register. We review the main experimental tools, and describe in detail unique aspects of the computer: the use of robust entangling gates and the development of a quantum coherent feedback system through EMCCD camera acquisition. The latter is necessary for carrying out quantum error correction protocols in future experiments.

quant-ph↗

arXiv2019

Theory of robust multi-qubit non-adiabatic gates for trapped-ions

The prevalent approach to executing quantum algorithms on quantum computers is to break-down the algorithms to a concatenation of universal gates, typically single and two-qubit gates. However such a decomposition results in long gate sequences which are exponential in the qubit register size. Furthermore, gate fidelities tend to decrease when acting in larger qubit registers. Thus high-fidelity implementations in large qubit registers is still a prominent challenge. Here we propose and investigate multi-qubit entangling gates for trapped-ions. Our gates couple many qubits at once, allowing to decrease the total number of gates used while retaining a high gate fidelity. Our method employs all of the normal-modes of motion of the ion chain, which allows to operate outside of the adiabatic regime and at rates comparable to the secular ion-trapping frequency. Furthermore we extend our method for generating Hamiltonians which are suitable for quantum analog simulations, such as a nearest-neighbour spin Hamiltonian or the Su-Schrieffer-Heeger Hamiltonian.

quant-ph↗