What Is Meant by Applied Quantum Computing?

H Hannan

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What Is Meant by Applied Quantum Computing?
Read More About Quantum Computing HERE.

Quantum computing is an exciting and rapidly advancing field expected to revolutionize certain areas of computing in the coming years. But much of the discussion around quantum computing centres on the hardware itself – the quantum bits (qubits) that can represent 0 and 1 simultaneously via superposition. However, putting quantum computers to practical use, or applied quantum computing, is crucial for the field to have a real-world impact. So what exactly is meant by applied quantum computing?

Defining Applied Quantum Computing

Simply put, applied quantum computing refers to using quantum computers, quantum circuits, and algorithms to solve meaningful real-world problems that could provide a competitive advantage or new capabilities not achievable or feasible with classical computing methods alone.

Rather than focusing solely on advancing the quantum hardware and low-level quantum assembly languages, applied quantum emphasized developing high-level algorithms, applications software stacks, and domain-specific use cases tailored to industries like finance, pharmaceuticals, energy, and logistics. Some examples of applied quantum computing include:

  • Using quantum machine learning models for pattern recognition in medical diagnosis.
  • Employing quantum simulation to model molecular interactions for drug discovery.
  • Programming optimization algorithms that harness quantum effects to improve supply chains.
  • Utilizing quantum random number generators for secure cryptographic key creation.

The contrast With Digital Computers

To better understand the unique opportunities of applied quantum computing, it helps to contrast it with the applied use of classical digital computers over the past few decades:

  1. Programming Languages and Software Stacks – Higher level languages, libraries, frameworks, and operating systems were developed to allow more focus on applications rather than circuits or transistors in digital computers. Quantum computing needs to accelerate the development of similar software capabilities.
  2. Killer Apps – Applied use cases like spreadsheets, word processors, computer graphics, etc popped up early on to demonstrate the real-world utility over just theoretical computation. Quantum needs signature applications that tap into quantum advantages.
  3. Abstraction of Hardware – Classical software development over time became less concerned with hardware changes. Quantum applications should similarly integrate cleanly with evolving qubit technologies.
  4. Problem-centric Focus – Digital computers found ubiquitous applied demand by solving business problems faster and cheaper. Quantum needs this problem-centric focus across sectors.

By leveraging learnings from classical digital technology adoption, investments in applied quantum computing research can speed up practical utilization and commercial viability.

Promising Near-Term Application Domains

While universal error-corrected quantum computers may be years away, presently available intermediate pre-threshold devices called Noisy Intermediate-Scale Quantum (NISQ) can start pioneering valuable applications. Some promising near-term domains include:

Quantum Chemistry: Exact modelling of molecular interactions for chemistry simulations using NISQ devices of just 50-100 qubits could already exceed classical computational limits. This can accelerate pharmaceutical drug discovery and material sciences. Startups like Xanadu and established players like IBM are active in this application area.

Optimization: Reformulating optimization challenges like supply chain logistics, financial portfolio management, and network flows as combinatorial problems represents an early opportunity for quantum advantage. D-Wave quantum annealers dedicated to optimization already demonstrate this. Menten AI and Riverlane are startups using quantum algorithms for optimization.

Quantum Machine Learning (QML): Employing a quantum circuit model of computation for machine learning tasks like pattern recognition, clustering, and neural networks can benefit from quantum superposition to exponentially represent feature spaces and quadratic speedups over classical ML. Amazon Braket, Google, IonQ, Pasqal, and Rahko are leveraging QML.

Security: Crypto-agility provided by Quantum Key Distribution (QKD) using photon polarization allows un-hackable cryptographic keys secured by laws of physics. ID Quantique is commercializing QKD. Quantum random number generators (QRNG) are also essential for cybersecurity and online gambling uses. Quantum eMotion and Quintessence Labs provide true QRNG solutions using quantum effects.

Sensing: Extremely precise quantum sensors exploiting entanglement and squeezed light can enable gravimeters, magnetometers, accelerometers, and atomic clocks to be useful for navigation systems, oil/mineral exploration, earthquake detection and financial trading accuracy. ColdQuanta, Honeywell, and NucQuantum lead in quantum sensing.

While universal error-corrected quantum computers will ultimately unlock exponential speedups to simulations and factoring-based cryptography, investing now in developing software stacks and applications tailored to NISQ devices can cement early high-value applied quantum advantage this decade across these areas.

Realizing Commercial Value from Quantum Investments

A 2019 BCG survey of early quantum technology adopters indicated computational chemistry and optimization as the top two application areas of interest. Yet software development was cited as the biggest overall challenge in applying quantum by the respondents. Further, a recent McKinsey report estimates that creating sustainable value from quantum will require patience and sustained investments similar to other foundational technologies like AI and blockchain.

To better realize ROI from quantum computing investments, businesses should:

  1. Identify computational bottlenecks faced today that quantum could potentially resolve. Formulate the problems abstractly to be hardware-agnostic.
  2. Survey the quantum software platforms, third-party quantum algorithm/application developers and quantum cloud service providers to kickstart development.
  3. Start with hybrid algorithms utilizing existing data/computing assets alongside quantum to reduce risk. Incrementally increase quantum workload percentage as the technology matures.
  4. Plan for talent re-skilling and cultural change management to make quantum computing more approachable for the wider IT organization beyond research.
  5. Actively participate in emerging industry standards organizations, consortiums and open-source software initiatives to hedge against vendor lock-in.

Positioning organizations both technically and culturally to prepare for and incrementally adopt quantum computing for targeted applications today can provide a head start on quantified business impact tomorrow as the technology achieves commercial scale.

The Future Is Bright for Applied Quantum

While quantum computing is still early on the hype cycle curve, steadfast progress in both hardware and software abstractions is bringing applied utilization within reach. Near-term applications in quantum chemistry, optimization, machine learning and security hold much promise to confer competitive advantages to enterprises proactively exploring quantum-enabled solutions for their industry. Patient investments today into developing high-level quantum application software and problem formulations will serve as a force multiplier when error-corrected quantum systems ultimately manifest and ushers in an era of exponential speedups. What was once just a theoretical possibility is fast becoming an applied reality in quantum computing.

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