Quantum computing hardware has matured significantly over the years, with several noisy intermediate-scale quantum (NISQ) computers already being available for public use in the cloud. Larger-scale hardware with capabilities such as quantum error correction and fault tolerant implementations of universal set of gates are currently being developed to support general purpose fault tolerant quantum computation. Quantum algorithms requiring general purpose FTQC, in the vein of the classic algorithms such as Shor’s algorithm for factorization, and Grover’s algorithm for search algorithm, continue to be developed. Additionally, in keeping with the trend of machine learning and AI in classical computing, quantum machine learning has emerged as a key application of quantum computers already in the NISQ era. Use cases range from quantum chemistry and drug design to financial portfolio optimization and fraud detection, and potentially greater, with potential for more in the upcoming fault tolerance era. Quantum computing also poses a significant threat to public key cryptography such as RSA and elliptic curve cryptography, warranting the development of post quantum cryptography.

It is likely that quantum computers for the foreseeable future will be housed in remote servers. While cloud access for current quantum computers is limited to classical instructions and responses since they are hosted over the “classical internet” that is only capable of transporting classical information bits, quantum networks capable of faithfully transferring quantum information in the form of qubits are being developed. The latter would enable distributed quantum information processing in general, including the exchange of quantum instructions or inputs and responses between cloud quantum servers hosting powerful quantum computers or trained QML models, and small-scale client quantum computers. 

Delegated and distributed quantum computation over both the present day classical networks and future quantum networks bring with it issues related to trust, privacy and security. Some examples include, from the client perspective, protection of intellectual property (IP) of quantum algorithms and quantum data; and from the server perspective, defense against as denial of service attacks, defense against tomographic attacks to steal information regarding hardware architectures, and QML model security. Examples of remedies include, on the software side, blind quantum computation, entanglement distillation in quantum networks, quantum network coding, and on the hardware side, forbidding access to pulse-level instruction inputs. It is imperative to develop such counter measures against these issues right away while quantum computing is still in its early days, which informs the goal of this workshop. 

Call for Papers

We invite submissions of previously unpublished works broadly in the areas of quantum computing, quantum machine learning, quantum networks, cybersecurity, and their interplay. Topics of interest include but are not limited to the following:

• Quantum computation

  • Blind quantum computation

  • Distributed quantum computing architectures

  • Quantum algorithms

  • Quantum communication complexity

  • Error correction and mitigation algorithms

  • NISQ and fault-tolerant applications

• Quantum machine learning

  • QML algorithms

  • QML applications

  • Quantum optimization (e.g., QAOA)

  • QML model security

  • Quantum data security

  • Training ML models

• Quantum networking & Cybersecurity

  • Quantum repeaters, switches, routers

  • Quantum data center architectures

  • Secure quantum networking

  • Quantum network coding

  • Quantum Key Distribution

  • Post quantum cryptography

Submission Deadline and Guidelines

• Deadline: August 31, 2024

• Notification of decision: September 10, 2024

• Final version due: September 20, 2024

• Paper submission guidelines same as for the IEEE TPS 2024

Organizing Committee

General Chair: Rob Cunningham, University of Pittsburgh

Program Committee (to be updated...)

Co-Chair: Kaushik P. Seshadreesan, University of Pittsburgh

Co-Chair: Junyu Liu, University of Pittsburgh

Jeff Prevost, University of Texas at San antonio

Paul Lopata, US Department of Defence

Karim Eldefrawy, SRI International