Papers for

quantum cloud service providers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Distributing quantum circuits faster using pre-shared connections

Distributing Quantum Circuits Using Pre-Distributed Entanglement Pairs over Quantum Networks

Abstract: Distributing quantum circuits across a network of quantum devices is a recent approach to improve the scalability of quantum computation. Entangled pairs of qubits distributed across network nodes are used as a communication resource for executing inter-node (remote) gates. However, establishing these entanglement pairs incurs significant latency, and doing so during the circuit's execution may lead to the decoherence of logical qubits. Instead, it may be beneficial to distribute certain entanglement pairs before the circuit is executed. Thus, we consider the problem of distributing a quantum circuit across a quantum network containing some pre-distributed entanglement pairs and seek to minimize the execution time of the circuit. We address this problem in three steps. First, given the quantum network and (optionally) a probability distribution over training circuits, we determine a set of pre-distributed entanglement pairs under a specified budget. Second, we compute a qubit-to-memory allocation that minimizes the expected execution time given these pre-distributed resources. Finally, we identify any additional entanglement pairs required during execution, generate them concurrently, and execute the circuit gates. We evaluate our methods on a broad range of benchmark circuits.

Mon 19 OctEmerging Technologies
The gist
Running quantum computations across multiple quantum devices can be slow because these devices need special linked qubit pairs called entanglement pairs, which take time to create. The authors look at how providing some of these entanglement pairs ahead of time helps make the overall computation faster. They develop a method to decide which pairs to prepare in advance and how to assign qubits to devices to speed up execution. Their approach also plans how to create any extra pairs needed during the process to minimize waiting times.
Open → 2610.26099v1

Gnn predicts quantum gate faults to improve circuit reliability

QUFIG: GNN-Based Prediction of Quantum Fault Injection Vulnerabilities with Gate-Level Precision

Abstract: The growing scale and accessibility of quantum hardware exposed new reliability and security challenges in the quantum computing workflow, such as the run-time fault injection attacks in cloud-based quantum computing platforms. However, existing works fail to identify vulnerabilities with gate-level precision or adapt to run-time environments. In this work, we formulate gate-level fault analysis as a learning-guided prioritization problem under restricted fidelity budgets. The framework uses a circuit-DAG-based GNN backbone to predict the vulnerability score of each gate to each type of injected fault, defined as the impact of the gate-fault pair on circuit fidelity. The gate-fault pairs are then ranked by their vulnerability score. Experiments on QASMbench and HamLib MaxCut show that QUFIG recovers high-impact vulnerable gate-fault pairs with fewer inspections than random and depth-based heuristics. Our results show that QUFIG can reduce the number of gates requiring inspection by 2.9--19.8% while maintaining effective fault identification, allowing quantum circuit designers to identify vulnerabilities and apply targeted defenses more efficiently.

Thu 1 OctCryptography and Security
The gist
Quantum computers can be vulnerable to faults that reduce their accuracy, especially when accessed remotely. The authors created a method called QUFIG that uses a special kind of neural network (a graph neural network) to figure out which parts of a quantum circuit are most likely to cause problems if they fail. This helps focus attention on the most risky areas without testing everything. Their tests on standard benchmarks show QUFIG can identify critical faulty spots more efficiently than random checks or simpler methods.
Open → 2610.01777v1