Why Selling Quantum Computing is Easier Than Selling a Quantum Computer
With dozens of start-ups in the emerging field of quantum computing, alongside giants like IBM, Microsoft, Amazon, and Google, the possibility of producing revenue from what is essentially a research and development venture is daunting. Publicly-traded quantum start-ups such as IonQ and D-Wave Systems, which do not have a non-quantum line of work to subsidize their investments, likely feel this stress more acutely.
Monetizing quantum technologies is hard as current systems have limited valuable capabilities. Significant advancements have been made in extending coherence times, reducing error rates, and boosting qubit counts; these developments affect product road maps for future systems. However, the promise of tomorrow has little influence on what is available currently.
Selling quantum computing widespread is possible, also when quantum computers still need to be advanced enough to be used in production. Doing so requires a dramatically different approach from hardware and software purveyors, along with industry-wide coordination– and restraint– in communicating the value of quantum technologies.
For quantum to prosper, the discourse around it must be debunked
The general public’s understanding of quantum computers’ utility and purpose remains abysmal. Possible buyers are reasonably better-off in spite of the marketing attempts of producers. Press releases proclaiming the achievement of “quantum supremacy” are as much a rite of passage as they are unhelpful to the cause; leaving alone the unfavorable ramifications of the word supremacy, the underlying claim has been so frequently duplicated and disproved that the premise itself is a thought-terminating cliché.
Explaining the value of quantum computers to possible customers must initially demystify the science behind the technology– phrases such as Einstein’s remark calling quantum entanglement “spooky action at a distance” is neither pertinent nor helpful in describing the worth of an error-corrected quantum computer.
Furthermore, inflating the practical ability of near-term quantum computers with subjective breakthroughs weakens the influence that higher qubit counts, robust error correction, greater qubit fidelity, and longer coherence times will ultimately deliver.
Quantum hardware producers need to release as many typical benchmarks as feasible. Individual metrics offer fascinating data points. Synthetic benchmarking enables progress to be gauged and tracked over time.
Quantum and classical synthetic benchmarks might not show a given system’s full value or ability, but this needs to not be utilized to dismiss existing standards. Introducing unique, company-specific benchmarks while ignoring competitors’ criteria would certainly make comparison almost impossible.
For quantum computing to be comprehended, it must be contextualized
Comparing successive quantum computers from one producer and between models from competing companies, is essential for characterizing progress. However, more is needed to transmit to prospective buyers what quantum computers can do or how they differ from classical computers. Oft-repeated practical examples– such as the difference between bits and qubits, the utility of entanglement, and so forth– explain how quantum computing differs from classical computing. However, it is frequently more conceptual than concrete.
Historically, classical computing developments have allowed affordable computation for different areas of mathematics. These methods enable new usages to which a computer can be practically applied, causing new products and abilities. These advances show the difference– and therefore, the worth– that quantum computers can give.
Classical processors, like the CPUs in consumer and enterprise systems today, are effectively the descendants of adding machines. Ever since the introduction of the Intel 8086 microprocessor in 1978 numerous improvements have been included, including longer bus widths, faster clock speeds, and also floating-point arithmetic.
Independent of architecture, any given application running on a CPU is by volume mainly the same six instructions: add, subtract, load, store, compare and branch. Traditional CPUs are purposefully general purpose; they can carry out virtually any calculation accurately, but not necessarily quickly.
CPUs could be more efficient at graphics processing, which needs higher parallelism and relies extensively on geometric calculations that CPUs are not suited for. Demand for 3D graphics processing in business and entertainment caused the mass-market commercialization of GPUs in the mid-1990s.
“Nice to have” attributes have been added with time, such as video encoding and decoding, texture mapping and raytracing. Nvidia’s CUDA software made its GPUs famous for general-purpose work, opening up the same underlying hardware to brand-new markets; comparatively, AMD’s software stack is much less flexible, and adoption of AMD GPUs past graphics processing is less common.
Higher demand for quantum computing
Artificial intelligence (AI) and machine learning workloads were the primary beneficiaries of non-graphical computing on GPUs, though this has actually not been a perfect fit. Although these work can use the parallelism of GPUs, they typically rely on matrix and tensor calculus and have a greater dependency on data locality than graphics processing.
In a similar way, extensions for texture mapping present in GPUs are not beneficial for AI or machine learning. In the mid-2010s, different approaches– such as Google’s Tensor Processing Unit and Graphcore’s Intelligence Processing Unit– surfaced as hardware accelerators to rectify this.
Quantum computing is the following step in this progression of computational capability, making it the fourth computing column. Quantum processors will all at once execute classes of applications that are not practical to calculate on classical computers.
The possibility of utilizing Shor’s algorithm to factor prime numbers in search of cracking encryption is typically promoted in security circles, but the effect of quantum computing extends past that.
Uses such as linear systems of equations, mathematical optimization, and boson sampling are believed to have ramifications for economics, engineering, and pharmacology, along with for the development of AI and machine learning normally.
For quantum computers to be valuable, quantum algorithms have to be developed in parallel
The ability of any computer platform is established by the quality and quantity of its software. For quantum hardware to be useful, quantum algorithms have to be developed in parallel. This requires time and cash, alongside a general idea of the troubles that could be resolved with a quantum computer. Crucially, what it does not call for is an expertise in quantum science; many manufacturer-specific and neutral tools are available to relieve the adoption process.
Handling these tools to ensure success is a feasible outcome requires breaking institutional issues such as internal opposition, culture clashes and budget tightening. Taking a wait-and-see strategy by definition delivers any potential of a first-mover advantage.
In a wider view of the potential influence of quantum computing, there is no guarantee that an issue relevant to a market will be resolved without a company placing in some effort to fix it. Or, to indulge in a truism– you miss 100% of the shots you do not take.
There is no panacea to resolving institutional inertia. Assuring businesses that quantum technologies are worth developing today is an important first step.
Fortunately, developing quantum software does not call for hand-stitching circuits for a specific computer. It is still very early days. Initiatives such as the QIR Alliance are developing cross-platform answers that intend to totally utilize the capabilities of quantum processors from different hardware producers.
Companies looking to explore quantum computing can associate with outside firms to get quantum competency or get guidance in developing quantum skills internally.
Read the original article on The Quantum Insider.
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