Quantum Inspire: QuTech’s platform for co-development and collaboration in quantum computing

Abstract

The mission of QuTech is to bring quantum technology to industry and society by translating fundamental scientific research into applied research. To this end we are developing Quantum Inspire (QI), a full-stack quantum computer prototype for future co-development and collaborative R&D in quantum computing.A prerelease of this prototype system is already offering the public cloud-based access to QuTech technologies such as a programmable quantum computer simulator (with up to 31 qubits) and tutorials and user background knowledge on quantum information science (www.quantum-inspire.com). Access to a programmable CMOS-compatible Silicon spin qubit-based quantum processor will be provided in the next deployment phase. The first generation of QI's quantum processors consists of a double quantum dot hosted in an in-house grown SiGe/ 28 Si/SiGe heterostructure, and defined with a single layer of Al gates.Here we give an overview of important aspects of the QI full-stack. We illustrate QI's modular system architecture and we will touch on parts of the manufacturing and electrical characterization of its first generation two spin qubit quantum processor unit. We close with a section on QI's qubit calibration framework. The definition of a single qubit Pauli X gate is chosen as concrete example of the matching of an experiment to a component of the circuit model for quantum computation.

Quantum approximate optimization for hard problems in linear algebra

“…One of the next steps would be to explore if QAOA can be valuable in applications that require BLLS as a subroutine, such as NBMF [17]. Another step can be to try the BLLS problem on other types of quantum computers [9][10][11][12][13] to see how different hardware implementations fare. Finally, from a practical standpoint, it may also be beneficial to apply a modified QAOAlike ansatz based on just nearest-neighbor connected CNOT gates, along with a larger number of parameters [99,100], or apply a SWAP-network QAOA approach [95].…”

“…And thus, the coefficients in Eqn(2) are found by expanding Eqn(A.4), which gives us Eqn (13) and Eqn (14). You will notice that there is a constant value from Eqn(A.4) that we leave out of Eqn (13) and Eqn (14).…”

“…And thus, the coefficients in Eqn(2) are found by expanding Eqn(A.4), which gives us Eqn (13) and Eqn (14). You will notice that there is a constant value from Eqn(A.4) that we leave out of Eqn (13) and Eqn (14). Because this value is not a coefficient for any of the variables, we can't optimize over it and it's left as is, which is b 2 2 .…”

“…The application of quantum computing to hard optimization problems is a candidate where quantum computing may eventually outperform classical computation [1][2][3][4][5][6]. At the time of writing this paper, noisy intermediate scale quantum (NISQ) computers [5] are being developed by several firms and research groups [7][8][9][10][11][12][13]. The two main approaches to quantum optimization are (i) the quantum annealing (QA) physical heuristic [6] and (ii) quantum approximate optimization algorithm (QAOA) [4] on the gate-model quantum computer [1].…”

Quantum computing hardware in the cloud: Should a computational chemist care?

“…In this Section, we are going to focus our attention on a particular type of semiconductor qubit system, which has been deployed for the realisation of the first spinbased quantum computer in the Cloud: SPIN-2QPU [95], developed at QuTech (a collaboration between TUDelft and TNO). It consists of two single electron spin qubits in a double quantum dot (DQD) that is electrostatically defined by metallic gate electrodes deposited on top of an isotopically purified Si/SiGe heterostructure, as illustrated in Fig.…”