Search arXivSearch

arXiv · 2110.10528

Detecting Entanglement Generating Circuits in Cloud-Based Quantum Computing

Abstract

Entanglement, a direct consequence of elementary quantum gates such as controlled-NOT or Toffoli gates, is a key resource that leads to quantum advantages. In this work, we establish the framework of certifying entanglement generation in cloud-based quantum computing services. Namely, we present the construction of quantum circuits that certify entanglement generation in a circuit-based quantum computing model. The framework relaxes the assumption of the so-called qubit allocation, which is the step in a cloud service to relate physical qubits in hardware to a circuit proposed by a user. Consequently, the certification is valid no matter how unsuccessful qubit allocations may be in cloud computing or how untrustful the service may be in qubit allocations. We then demonstrate the certification of entanglement generation on two and three qubits in the IBMQ and IonQ services. Remarkably, entanglement generation is successfully certified in the IonQ service that does not provide a command of qubit allocations. The capabilities of entanglement generation in the circuits of IBMQ and IonQ are also quantified. We envisage that the proposed framework is applied when cloud-based quantum computing services are exploited for practical computation and information tasks, for which our results would find if it is possible to achieve quantum advantages.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jiheon Seong, Joonwoo Bae. 2021-10-20. Detecting Entanglement Generating Circuits in Cloud-Based Quantum Computing. https://doi.org/10.34133/icomputing.0051

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Fermionic magic resources in disordered quantum spin chains

Fermionic non-Gaussianity quantifies a quantum state's deviation from a classically tractable free-fermionic description, constituting a necessary resource for computational quantum advantage. Here we use fermionic antiflatness (FAF) to measure this deviation across ergodic and many-body localized (MBL) regimes. We focus on the paradigmatic disordered spin-$1\!/2$ XXZ chain and its impurity variant with local interactions. Across highly excited eigenstates, FAF evolves from typical-state behavior at weak disorder to strongly suppressed values deep in the MBL regime, with volume-law scaling in the XXZ chain and an area-law bound in the impurity setting. Rare long-range cat-like eigenstates exhibit a pronounced enhancement of FAF, making it a sensitive diagnostic of mechanisms proposed to destabilize MBL. Starting from product states, we find that in the MBL regime FAF grows slowly in time, approaching saturation via a power-law relaxation. Overall, our results show that MBL suppresses fermionic non-Gaussianity, and the associated complexity beyond free fermions, while ergodicity restores it, motivating explorations of fermionic non-Gaussianity in other ergodicity-breaking phenomena.

quant-ph

Progressive Binarization - Pauli Correlation Encoding: a Continuation Method for Constrained Optimization

Pauli Correlation Encoding (PCE) reduces the qubit requirements of quantum optimization by embedding the problem variables into the expectation values of Pauli observables, so that the number of qubits can be much smaller than the number of variables. PCE has not yet been studied for constrained optimization. We extend it to constrained combinatorial problems, using the budget-constrained MinCut as a case study, and show that the standard formulation fails to reliably enforce the constraint: feasibility hinges on the binarization of the encoded variables, which depends sensitively on hyperparameters that are hard to tune and do not transfer across instances. To address this, we introduce Progressive-Binarization PCE (PB-PCE), an adaptive continuation scheme that progressively increases the binarization parameter while re-optimizing the circuit from the previous solution, driving the variables towards the binary domain. PB-PCE attains near-complete constraint satisfaction (88--100\%) and smaller cut sizes than standard PCE, with a number of stages (10--20) essentially independent of problem size, solving instances of up to 300 variables with only 9-qubit circuits.

quant-ph

A quantum model for synchronizing finite state transition systems

We propose a quantum model for finding a resetting input sequence (RS) which can take a finite state transition system (FA), to particular state independent of its current state. The complexity of finding such sequences for various types of FA can be NP-Hard or even PSPACE-Complete. To this end, we represent the FA states, inputs, and transition function in quantum space. Accordingly, we propose a model to represent the execution of an input sequence of a particular length $l$ starting form an initial FA state. The model is extended considering the application in superposition of all input sequences of length $l$ to an initial state of the FA. The model is further extended considering the application of all input sequences to all initial states of the FA capturing for every input sequence the collection (ordered list) of states reached by applying the sequence to all states of the FA. The amplitude amplification algorithm is then used as it combines similar collections of reached states while preserving all input sequences that reach these collections. A Grover search for a reached collection where its elements correspond to the same FA state provides a RS for the FA. Our approach offers a quadratic gain over the exponential complexity of traditional brute-force method, which is the only method that can be applied to a general FA class.

quant-ph